Image-Based Crack Detection Methods: A Review
Annually, millions of dollars are spent to carry out defect detection in key infrastructure including roads, bridges, and buildings. The aftermath of natural disasters like floods and earthquakes leads to severe damage to the urban infrastructure. Maintenance operations that follow for the damaged i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Infrastructures |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2412-3811/6/8/115 |
| _version_ | 1827685089363886080 |
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| author | Hafiz Suliman Munawar Ahmed W. A. Hammad Assed Haddad Carlos Alberto Pereira Soares S. Travis Waller |
| author_facet | Hafiz Suliman Munawar Ahmed W. A. Hammad Assed Haddad Carlos Alberto Pereira Soares S. Travis Waller |
| author_sort | Hafiz Suliman Munawar |
| collection | DOAJ |
| description | Annually, millions of dollars are spent to carry out defect detection in key infrastructure including roads, bridges, and buildings. The aftermath of natural disasters like floods and earthquakes leads to severe damage to the urban infrastructure. Maintenance operations that follow for the damaged infrastructure often involve a visual inspection and assessment of their state to ensure their functional and physical integrity. Such damage may appear in the form of minor or major cracks, which gradually spread, leading to ultimate collapse or destruction of the structure. Crack detection is a very laborious task if performed via manual visual inspection. Many infrastructure elements need to be checked regularly and it is therefore not feasible as it will require significant human resources. This may also result in cases where cracks go undetected. A need, therefore, exists for performing automatic defect detection in infrastructure to ensure its effectiveness and reliability. Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. This paper provides a review of image-based crack detection techniques which implement image processing and/or machine learning. A total of 30 research articles have been collected for the review which is published in top tier journals and conferences in the past decade. A comprehensive analysis and comparison of these methods are performed to highlight the most promising automated approaches for crack detection. |
| first_indexed | 2024-03-10T08:43:06Z |
| format | Article |
| id | doaj.art-9bfd0eb399dc4a8d8f59df40197ec7f5 |
| institution | Directory Open Access Journal |
| issn | 2412-3811 |
| language | English |
| last_indexed | 2024-03-10T08:43:06Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj.art-9bfd0eb399dc4a8d8f59df40197ec7f52023-11-22T08:06:30ZengMDPI AGInfrastructures2412-38112021-08-016811510.3390/infrastructures6080115Image-Based Crack Detection Methods: A ReviewHafiz Suliman Munawar0Ahmed W. A. Hammad1Assed Haddad2Carlos Alberto Pereira Soares3S. Travis Waller4School of Built Environment, University of New South Wales, Kensington, Sydney, NSW 2052, AustraliaSchool of Built Environment, University of New South Wales, Kensington, Sydney, NSW 2052, AustraliaPrograma de Engenharia Ambiental, PEA/POLI & EQ, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-909, BrazilPós-Graduação em Engenharia Civil, Universidade Federal Fluminense, Niterói 24210-240, BrazilSchool of Civil and Environmental Engineering, University of New South Wales, Kensington, Sydney, NSW 2052, AustraliaAnnually, millions of dollars are spent to carry out defect detection in key infrastructure including roads, bridges, and buildings. The aftermath of natural disasters like floods and earthquakes leads to severe damage to the urban infrastructure. Maintenance operations that follow for the damaged infrastructure often involve a visual inspection and assessment of their state to ensure their functional and physical integrity. Such damage may appear in the form of minor or major cracks, which gradually spread, leading to ultimate collapse or destruction of the structure. Crack detection is a very laborious task if performed via manual visual inspection. Many infrastructure elements need to be checked regularly and it is therefore not feasible as it will require significant human resources. This may also result in cases where cracks go undetected. A need, therefore, exists for performing automatic defect detection in infrastructure to ensure its effectiveness and reliability. Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. This paper provides a review of image-based crack detection techniques which implement image processing and/or machine learning. A total of 30 research articles have been collected for the review which is published in top tier journals and conferences in the past decade. A comprehensive analysis and comparison of these methods are performed to highlight the most promising automated approaches for crack detection.https://www.mdpi.com/2412-3811/6/8/115crack detectionmachine learningartificial intelligenceimage processing |
| spellingShingle | Hafiz Suliman Munawar Ahmed W. A. Hammad Assed Haddad Carlos Alberto Pereira Soares S. Travis Waller Image-Based Crack Detection Methods: A Review Infrastructures crack detection machine learning artificial intelligence image processing |
| title | Image-Based Crack Detection Methods: A Review |
| title_full | Image-Based Crack Detection Methods: A Review |
| title_fullStr | Image-Based Crack Detection Methods: A Review |
| title_full_unstemmed | Image-Based Crack Detection Methods: A Review |
| title_short | Image-Based Crack Detection Methods: A Review |
| title_sort | image based crack detection methods a review |
| topic | crack detection machine learning artificial intelligence image processing |
| url | https://www.mdpi.com/2412-3811/6/8/115 |
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